Computer Science

What is a visualization? There is limited utility in trifling with definitions, except insofar as one serves as a tool for communicating and conceptualizing our subject matter; a statement of identity for a community. To establish Visualization Psychology as a viable inter-disciplinary research programme, we must first define the object(s) of our collective inquiry. I propose that while we might refer to the study of "visualization" for the term's colloquial accessibility and pragmatic alignment with other fields, we should consider for exploration a class of artifacts and corresponding processes more expansive and profound: external representations. What follows is an argument for the study of external representation as the foundation for a new interdisciplinary endeavor, and approach to mapping the corresponding problem space.

Embodied Conversational Agents (ECA) take on different forms, including virtual avatars or physical agents, such as a humanoid robot. ECAs are often designed to produce nonverbal behaviour to complement or enhance its verbal communication. One form of nonverbal behaviour is co-speech gesturing, which involves movements that the agent makes with its arms and hands that is paired with verbal communication. Co-speech gestures for ECAs can be created using different generation methods, such as rule-based and data-driven processes. However, reports on gesture generation methods use a variety of evaluation measures, which hinders comparison. To address this, we conducted a systematic review on co-speech gesture generation methods for iconic, metaphoric, deictic or beat gestures, including their evaluation methods. We reviewed 22 studies that had an ECA with a human-like upper body that used co-speech gesturing in a social human-agent interaction, including a user study to evaluate its performance. We found most studies used a within-subject design and relied on a form of subjective evaluation, but lacked a systematic approach. Overall, methodological quality was low-to-moderate and few systematic conclusions could be drawn. We argue that the field requires rigorous and uniform tools for the evaluation of co-speech gesture systems. We have proposed recommendations for future empirical evaluation, including standardised phrases and test scenarios to test generative models. We have proposed a research checklist that can be used to report relevant information for the evaluation of generative models as well as to evaluate co-speech gesture use.

Research has shown that trust is an essential aspect of human-computer interaction directly determining the degree to which the person is willing to use a system. An automatic prediction of the level of trust that a user has on a certain system could be used to attempt to correct potential distrust by having the system take relevant actions like, for example, apologizing or explaining its decisions. In this work, we explore the feasibility of automatically detecting the level of trust that a user has on a virtual assistant (VA) based on their speech. We developed a novel protocol for collecting speech data from subjects induced to have different degrees of trust in the skills of a VA. The protocol consists of an interactive session where the subject is asked to respond to a series of factual questions with the help of a virtual assistant. In order to induce subjects to either trust or distrust the VA's skills, they are first informed that the VA was previously rated by other users as being either good or bad; subsequently, the VA answers the subjects' questions consistently to its alleged abilities. All interactions are speech-based, with subjects and VAs communicating verbally, which allows the recording of speech produced under different trust conditions. Using this protocol, we collected a speech corpus in Argentine Spanish. We show clear evidence that the protocol effectively succeeded in influencing subjects into the desired mental state of either trusting or distrusting the agent's skills, and present results of a perceptual study of the degree of trust performed by expert listeners. Finally, we found that the subject's speech can be used to detect which type of VA they were using, which could be considered a proxy for the user's trust toward the VA's abilities, with an accuracy up to 76%, compared to a random baseline of 50%.

Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during model building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

Remote collaboration systems have become increasingly important in today's society, especially during times where physical distancing is advised. Industry, research and individuals face the challenging task of collaborating and networking over long distances. While video and teleconferencing are already widespread, collaboration systems in augmented, virtual, and mixed reality are still a niche technology. We provide an overview of recent developments of synchronous remote collaboration systems and create a taxonomy by dividing them into three main components that form such systems: Environment, Avatars, and Interaction. A thorough overview of existing systems is given, categorising their main contributions in order to help researchers working in different fields by providing concise information about specific topics such as avatars, virtual environment, visualisation styles and interaction. The focus of this work is clearly on synchronised collaboration from a distance. A total of 82 unique systems for remote collaboration are discussed, including more than 100 publications and 25 commercial systems.

In many instances of online collaboration, ideation and deliberation about what to write happen separately from the synthesis of the deliberation into a cohesive document. However, this may result in a final document that has little connection to the discussion that came before. In this work, we present interleaved discussion and summarization, a process where discussion and summarization are woven together in a single space, and collaborators can switch back and forth between discussing ideas and summarizing discussion until it results in a final document that incorporates and references all discussion points. We implement this process into a tool called Wikum+ that allows groups working together on a project to create living summaries-artifacts that can grow as new collaborators, ideas, and feedback arise and shrink as collaborators come to consensus. We conducted studies where groups of six people each collaboratively wrote a proposal using Wikum+ and a proposal using a messaging platform along with Google Docs. We found that Wikum+'s integration of discussion and summarization helped users be more organized, allowing for light-weight coordination and iterative improvements throughout the collaboration process. A second study demonstrated that in larger groups, Wikum+ is more inclusive of all participants and more comprehensive in the final document compared to traditional tools.

The objective of this study is to develop and use a virtual reality game as a tool to assess the effects of realistic stress on the behavioral and physiological responses of participants. The game is based on a popular Steam game called Keep Talking Nobody Explodes, where the player collaborates with another person to defuse a bomb. Varying levels of difficulties in solving a puzzle and time pressures will result in different stress levels that can be measured in terms of errors, response time lengths, and other physiological measurements. The game was developed using 3D programming tools including Blender and virtual reality development kit (VRTK). To measure response times accurately, we added LSL (Lab Stream Layer) Markers to collect and synchronize physiological signals, behavioral data, and the timing of game events. We recorded Electrocardiogram (ECG) data during gameplay to assess heart rate and heart-rate variability (HRV) that have been shown as reliable indicators of stress. Our empirical results showed that heart rate increased significantly while HRV reduced significantly when the participants under high stress, which are consistent with the prior mainstream stress research. We further experimented with other tools to enhance communication between two players under adverse conditions and found that an automatic speech recognition software effectively enhanced the communication between the players by displaying keywords into the player's headset that lead to the facilitation of finding the solution of the puzzles or modules. This VR game framework is publicly available in Github and allows researchers to measure and synchronize other physiological signals such as electroencephalogram, electromyogram, and pupillometry.

Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring causal relations from data, domain practitioners still lack effective visual interface for interpreting the causal relations and applying them in their decision-making process. Through interview studies with domain experts, we characterize their current decision-making workflows, challenges, and needs. Through an iterative design process, we developed a visualization tool that allows analysts to explore, validate, and apply causal relations in real-world decision-making scenarios. The tool provides an uncertainty-aware causal graph visualization for presenting a large set of causal relations inferred from high-dimensional data. On top of the causal graph, it supports a set of intuitive user controls for performing what-if analyses and making action plans. We report on two case studies in marketing and student advising to demonstrate that users can effectively explore causal relations and design action plans for reaching their goals.

Autonomous multi-robot systems, where a team of robots shares information to perform tasks that are beyond an individual robot's abilities, hold great promise for a number of applications, such as planetary exploration missions. Each robot in a multi-robot system that uses the shared-world coordination paradigm autonomously schedules which robot should perform a given task, and when, using its worldview--the robot's internal representation of its belief about both its own state, and other robots' states. A key problem for operators is that robots' worldviews can fall out of sync (often due to weak communication links), leading to desynchronization of the robots' scheduling decisions and inconsistent emergent behavior (e.g., tasks not performed, or performed by multiple robots). Operators face the time-consuming and difficult task of making sense of the robots' scheduling decisions, detecting de-synchronizations, and pinpointing the cause by comparing every robot's worldview. To address these challenges, we introduce MOSAIC Viewer, a visual analytics system that helps operators (i) make sense of the robots' schedules and (ii) detect and conduct a root cause analysis of the robots' desynchronized worldviews. Over a year-long partnership with roboticists at the NASA Jet Propulsion Laboratory, we conduct a formative study to identify the necessary system design requirements and a qualitative evaluation with 12 roboticists. We find that MOSAIC Viewer is faster- and easier-to-use than the users' current approaches, and it allows them to stitch low-level details to formulate a high-level understanding of the robots' schedules and detect and pinpoint the cause of the desynchronized worldviews.

Online exams have become widely used to evaluate students' performance in mastering knowledge in recent years, especially during the pandemic of COVID-19. However, it is challenging to conduct proctoring for online exams due to the lack of face-to-face interaction. Also, prior research has shown that online exams are more vulnerable to various cheating behaviors, which can damage their credibility. This paper presents a novel visual analytics approach to facilitate the proctoring of online exams by analyzing the exam video records and mouse movement data of each student. Specifically, we detect and visualize suspected head and mouse movements of students in three levels of detail, which provides course instructors and teachers with convenient, efficient and reliable proctoring for online exams. Our extensive evaluations, including usage scenarios, a carefully-designed user study and expert interviews, demonstrate the effectiveness and usability of our approach.